Abundances and search for vertical stratification in the atmospheres of four HgMn stars
Bibliographic record
Abstract
Using high-resolution, high-signal-to-noise ratio archival UVES (Ultraviolet-Visual Echelle Spectrograph) spectra, we have performed a detailed spectroscopic analysis of four chemically peculiar HgMn stars (HD 71066, HD 175640, HD 178065 and HD 221507). Using spectrum synthesis, mean photospheric chemical abundances are derived for 22 ions of 16 elements. We find good agreement between our derived abundances and those published previously by other authors. For the five elements that present a sufficient number of suitable lines, we have attempted to detect vertical chemical stratification by analyzing the dependence of derived abundance as a function of optical depth. For most elements and most stars, we find no evidence of chemical stratification with typical 3σ upper limits of Δ log Nelem/Ntot∼ 0.1–0.2 dex per unit optical depth. However, for Mn in the atmosphere of HD 178065 we find convincing evidence of stratification. Modelling of the line profiles using a two-step model for the abundance of Mn yields a local abundance varying approximately linearly by ∼0.7 dex through the optical depth range log τ5000=−3.6 to −2.8.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".